The selection of the dimensionality for word2vec is important with regard to two aspects: model accuracy and computing resources. It is crucial to have a model of dimensionality high enough to learn the regularity of the data, but too high a dimensionality tends to cause overfitting. In addition, a large model is accompanied by a massive number of parameters for storage in the machine during training [1]}, leading to wasted memory resources. Thus, it is crucial to devise a method that can decide upon a dimensionality that satisfies the ability to capture necessary information from training data as well as makes efficient use of the computational resources.
